avgScalarField = 0.5*(scalarField + scalarFieldShifted)
avgGaugeField = FieldTools.linearAverage(gaugeField, gaugeFieldShifted)
interpScalarVals = tf.gather_nd(avgScalarField, inputIndices)
interpGaugeVals = tf.gather_nd(avgGaugeField, inputIndices)
outputIndexVectorsEven = inputIndexVectors.copy()
outputIndexVectorsEven[axis] = 2*inputIndexVectors[axis]
outputIndicesEven = tf.stack(tf.meshgrid(outputIndexVectorsEven[0], outputIndexVectorsEven[1], outputIndexVectorsEven[2], indexing="ij"), -1)
outputScalarField = tf.tensor_scatter_nd_update(outputScalarField, outputIndicesEven, interpScalarVals)
outputGaugeField = tf.tensor_scatter_nd_update(outputGaugeField, outputIndicesEven, interpGaugeVals)
return outputScalarField, outputGaugeField
B = 9
smallMagneticField = FieldTools.constantMagneticField(
inputR, inputY, inputZ, 0, -B
)
inputGaugeField = FieldTools.linearSuperpose(
inputGaugeField, smallMagneticField
)
outputScalarField, outputGaugeField = interp1d(inputScalarField, inputGaugeField, 0, theory)
outputScalarField, outputGaugeField = interp1d(outputScalarField, outputGaugeField, 1, theory)
outputScalarField, outputGaugeField = interp1d(outputScalarField, outputGaugeField, 2, theory)
outputLatShape = tf.shape(outputScalarField)[0:3]
outputParams = inputParams
outputParams["vev"] /= 2
outputParams["latShape"] = outputLatShape
r = tf.cast(
for ii in range(shiftNumRight):
rightGaugeField = singlePoleTheory.shiftGaugeField(rightGaugeField, 0, +1)
rightScalarField = singlePoleTheory.shiftScalarField(
rightScalarField, 0, +1)
gaugeField = FieldTools.linearSuperpose(leftGaugeField, rightGaugeField)
scalarField = 0.5 * (leftScalarField + rightScalarField)
# Shift once more to centre the pair
gaugeField = tf.roll(gaugeField, -1, 0)
scalarField = tf.roll(scalarField, -1, 0)
numFluxQuanta = args.externalField
# Negative flux quanta results in lowering of energy (dipole points along
# negative x axis)
magField = FieldTools.constantMagneticField(X, Y, Z, 0, -numFluxQuanta)
gaugeField = FieldTools.linearSuperpose(gaugeField, magField)
gaugeFieldVar = tf.Variable(gaugeField, trainable=True)
scalarFieldVar = tf.Variable(scalarField, trainable=True)
params = params = {
"vev": args.vev,
"selfCoupling": args.selfCoupling,
"gaugeCoupling": args.gaugeCoupling,
"boundaryConditions": [0, 0, 0]
}
theory = GeorgiGlashowSu2TheoryUnitary(params)
inputPath = args.inputPath
inputR = tf.constant(np.load(inputPath + "/R.npy", allow_pickle=True))
inputY = tf.constant(np.load(inputPath + "/Y.npy", allow_pickle=True))
inputZ = tf.constant(np.load(inputPath + "/Z.npy", allow_pickle=True))
inputScalarField = np.load(inputPath + "/scalarField.npy", allow_pickle=True)
inputGaugeField = np.load(inputPath + "/gaugeField.npy", allow_pickle=True)
inputParams = np.load(inputPath + "/params.npy", allow_pickle=True).item()
inputLatShape = inputParams["latShape"]
yzPaddings = [[0, 0], [1, 1], [1, 1], [0, 0], [0, 0]]
outputScalarField = inputScalarField
outputGaugeField = inputGaugeField
B = 10
smallMagneticField = FieldTools.constantMagneticField(inputR, inputY, inputZ,
0, -B)
# Subtract the original field so the padding works; this will be added back
outputGaugeField = FieldTools.linearSuperpose(outputGaugeField,
smallMagneticField)
for ii in range(inputLatShape[1] // 2):
outputScalarField = tf.pad(outputScalarField, yzPaddings, "symmetric")
outputGaugeFieldR = tf.pad(outputGaugeField[:, :, :, 0, :, :], yzPaddings,
"symmetric")
outputGaugeFieldY = tf.pad(outputGaugeField[:, :, :, 1, :, :], yzPaddings,
"symmetric")
outputGaugeFieldZ = tf.pad(outputGaugeField[:, :, :, 2, :, :], yzPaddings,
"symmetric")
outputGaugeField = tf.stack(
[outputGaugeFieldR, outputGaugeFieldY, outputGaugeFieldZ], -3)